1,531 research outputs found
Deterministic Factorization of Sparse Polynomials with Bounded Individual Degree
In this paper we study the problem of deterministic factorization of sparse
polynomials. We show that if is a
polynomial with monomials, with individual degrees of its variables bounded
by , then can be deterministically factored in time . Prior to our work, the only efficient factoring algorithms known for
this class of polynomials were randomized, and other than for the cases of
and , only exponential time deterministic factoring algorithms were
known.
A crucial ingredient in our proof is a quasi-polynomial sparsity bound for
factors of sparse polynomials of bounded individual degree. In particular we
show if is an -sparse polynomial in variables, with individual
degrees of its variables bounded by , then the sparsity of each factor of
is bounded by . This is the first nontrivial bound on
factor sparsity for . Our sparsity bound uses techniques from convex
geometry, such as the theory of Newton polytopes and an approximate version of
the classical Carath\'eodory's Theorem.
Our work addresses and partially answers a question of von zur Gathen and
Kaltofen (JCSS 1985) who asked whether a quasi-polynomial bound holds for the
sparsity of factors of sparse polynomials
Derandomization via Symmetric Polytopes: Poly-Time Factorization of Certain Sparse Polynomials
More than three decades ago, after a series of results, Kaltofen and Trager (J. Symb. Comput. 1990) designed a randomized polynomial time algorithm for factorization of multivariate circuits. Derandomizing this algorithm, even for restricted circuit classes, is an important open problem. In particular, the case of s-sparse polynomials, having individual degree d = O(1), is very well-studied (Shpilka, Volkovich ICALP\u2710; Volkovich RANDOM\u2717; Bhargava, Saraf and Volkovich FOCS\u2718, JACM\u2720). We give a complete derandomization for this class assuming that the input is a symmetric polynomial over rationals. Generally, we prove an s^poly(d)-sparsity bound for the factors of symmetric polynomials over any field. This characterizes the known worst-case examples of sparsity blow-up for sparse polynomial factoring.
To factor f, we use techniques from convex geometry and exploit symmetry (only) in the Newton polytope of f. We prove a crucial result about convex polytopes, by introducing the concept of "low min-entropy", which might also be of independent interest
On Some Computations on Sparse Polynomials
In arithmetic circuit complexity the standard operations are +,x. Yet, in some scenarios exponentiation gates are considered as well. In this paper we study the question of efficiently evaluating a polynomial given an oracle access to its power. Among applications, we show that:
* A reconstruction algorithm for a circuit class c can be extended to handle f^e for f in C.
* There exists an efficient deterministic algorithm for factoring sparse multiquadratic polynomials.
* There is a deterministic algorithm for testing a factorization of sparse polynomials, with constant individual degrees, into sparse irreducible factors. That is, testing if f = g_1 x ... x g_m when f has constant individual degrees and g_i-s are irreducible.
* There is a deterministic reconstruction algorithm for multilinear depth-4 circuits with two multiplication gates.
* There exists an efficient deterministic algorithm for testing whether two powers of sparse polynomials are equal. That is, f^d = g^e when f and g are sparse
Deterministically Factoring Sparse Polynomials into Multilinear Factors and Sums of Univariate Polynomials
We present the first efficient deterministic algorithm for factoring sparse polynomials that split into multilinear factors
and sums of univariate polynomials. Our result makes partial progress towards the resolution of the classical question posed by von zur Gathen and Kaltofen in [von zur Gathen/Kaltofen, J. Comp. Sys. Sci., 1985] to devise an efficient deterministic algorithm for factoring (general) sparse polynomials. We achieve our goal by introducing essential factorization schemes which can be thought of as a relaxation of the regular factorization notion
Near-optimal Bootstrapping of Hitting Sets for Algebraic Models
The classical lemma of Ore-DeMillo-Lipton-Schwartz-Zippel
[Ore22,DL78,Zip79,Sch80] states that any nonzero polynomial of degree at most will evaluate to a nonzero value at some point on a
grid with . Thus, there is an explicit
hitting set for all -variate degree , size algebraic circuits of size
.
In this paper, we prove the following results:
- Let be a constant. For a sufficiently large constant and
all , if we have an explicit hitting set of size
for the class of -variate degree polynomials that are computable by
algebraic circuits of size , then for all , we have an explicit hitting
set of size for -variate circuits of
degree and size . That is, if we can obtain a barely non-trivial
exponent compared to the trivial sized hitting set even for
constant variate circuits, we can get an almost complete derandomization of
PIT.
- The above result holds when "circuits" are replaced by "formulas" or
"algebraic branching programs".
This extends a recent surprising result of Agrawal, Ghosh and Saxena [AGS18]
who proved the same conclusion for the class of algebraic circuits, if the
hypothesis provided a hitting set of size at most
(where is any constant). Hence, our work significantly weakens the
hypothesis of Agrawal, Ghosh and Saxena to only require a slightly non-trivial
saving over the trivial hitting set, and also presents the first such result
for algebraic branching programs and formulas.Comment: The main result has been strengthened significantly, compared to the
older version of the paper. Additionally, the stronger theorem now holds even
for subclasses of algebraic circuits, such as algebraic formulas and
algebraic branching program
Factors of Low Individual Degree Polynomials
In [Kaltofen, 1989], Kaltofen proved the remarkable fact that multivariate polynomial factorization can be done efficiently, in randomized polynomial time. Still, more than twenty years after Kaltofen\u27s work, many questions remain unanswered regarding the complexity aspects of polynomial factorization, such as the question of whether factors of polynomials efficiently computed by arithmetic formulas also have small arithmetic formulas, asked in [Kopparty/Saraf/Shpilka,CCC\u2714], and the question of bounding the depth of the circuits computing the factors of a polynomial.
We are able to answer these questions in the affirmative for the interesting class of polynomials of bounded individual degrees, which contains polynomials such as the determinant and the permanent. We show that if P(x_1, ..., x_n) is a polynomial with individual degrees bounded by r that can be computed by a formula of size s and depth d, then any factor f(x_1, ..., x_n) of P(x_1, ..., x_n) can be computed by a formula of size poly((rn)^r, s) and depth d+5. This partially answers the question above posed in [Kopparty/Saraf/Shpilka,CCC\u2714], that asked if this result holds without the exponential dependence on r. Our work generalizes the main factorization theorem from Dvir et al. [Dvir/Shpilka/Yehudayoff,SIAM J. Comp., 2009], who proved it for the special case when the factors are of the form f(x_1, ..., x_n) = x_n - g(x_1, ..., x_n-1). Along the way, we introduce several new technical ideas that could be of independent interest when studying arithmetic circuits (or formulas)
Efficiently Detecting Torsion Points and Subtori
Suppose X is the complex zero set of a finite collection of polynomials in
Z[x_1,...,x_n]. We show that deciding whether X contains a point all of whose
coordinates are d_th roots of unity can be done within NP^NP (relative to the
sparse encoding), under a plausible assumption on primes in arithmetic
progression. In particular, our hypothesis can still hold even under certain
failures of the Generalized Riemann Hypothesis, such as the presence of
Siegel-Landau zeroes. Furthermore, we give a similar (but UNconditional)
complexity upper bound for n=1. Finally, letting T be any algebraic subgroup of
(C^*)^n we show that deciding whether X contains T is coNP-complete (relative
to an even more efficient encoding),unconditionally. We thus obtain new
non-trivial families of multivariate polynomial systems where deciding the
existence of complex roots can be done unconditionally in the polynomial
hierarchy -- a family of complexity classes lying between PSPACE and P,
intimately connected with the P=?NP Problem. We also discuss a connection to
Laurent's solution of Chabauty's Conjecture from arithmetic geometry.Comment: 21 pages, no figures. Final version, with additional commentary and
references. Also fixes a gap in Theorems 2 (now Theorem 1.3) regarding
translated subtor
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